Advertisement generation and optimization

ABSTRACT

Methods, apparatuses, and articles of manufacture for generating advertisements using an algorithmic system, such as a combinatoric system, and determining effectiveness metrics or predictions for the advertisements are described herein.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 11/741,183, entitled “Advertisement Generation andOptimization” and filed Apr. 27, 2007, which claims the benefit of U.S.Provisional Applications 60/795,416, entitled “Advertisement Generationand Optimization”, filed on Apr. 27, 2006 and 60/827,252, entitled“Dynamic Advertisement Generation”, filed on Sep. 28, 2006. Thespecification of the 60/795,416 and 60/827,252 provisional applicationsare hereby fully incorporated by reference.

FIELD

The present invention relates generally to data processing. Morespecifically, the present invention relates to generating advertisementsusing an algorithmic system, and determining effectiveness metrics orpredictions for the generated advertisements.

BACKGROUND

Search keyword advertisements may be associated with an ad copy. An adcopy may be a few lines of text that are in turn associated with a linkto a bidding merchant's website. The performance/effectiveness of anadvertisement may often depend on the associated ad copy. The few wordscomprising an ad copy, such as “Find Amazing Interest Rates & LowCosts—Apply Now for Fast Approval,” may be the subject of substantialthought by copy-writers, who may manually generate the ad copy. Such adcopy, however, are typically selected with little or no empirical basisto predict or validate their effectiveness.

Similar problems are also posed by advertisements present in other mediaobjects, such as television programs and video games. Various aspects ofan advertisement in a media object, such as the speed of a car in atelevision advertisement, and the color of the car, may also requiresubstantial efforts by advertisers, and may also lack an empirical basisfor predicting or validating their effectiveness.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be described by way of exemplary embodiments,but not limitations, illustrated in the accompanying drawings in whichlike references denote similar elements, and in which:

FIG. 1 illustrates an overview of the invention, in accordance withvarious embodiments;

FIGS. 2 a-2 b are flow charts depicting various embodiments of theinvention; and

FIG. 3 illustrates an exemplary computing device capable of performingthe operations of various embodiments of the present invention.

DETAILED DESCRIPTION

Illustrative embodiments of the present invention include but are notlimited to methods and apparatuses for receiving, by a computing device,at least one algorithmic expression, such as a combinatoric expression,for a plurality of advertisements, defined based on one or both of userinputs and/or computational-linguistic methods. In various embodiments,the computing device may then analyze the at least one algorithmicexpression in view of one or more models of advertisement effectivenessto determine a subset of one or more advertisements of the plurality ofadvertisements as optimal among the plurality, (e.g. having the highestlikelihood of effectiveness in accordance with an effectivenessmodel/function under certain constraints, such as cost). In otherembodiments, a computer system may receive an advertisement generatedbased on such algorithmic expressions and evaluations for the pluralityof advertisements or so generate such an advertisement. Also, in variousembodiments, the computing device may then deploy and validate the“optimal” advertisement(s) by determining one or more metrics ofeffectiveness for the advertisement based on user reactions to theadvertisement.

Various aspects of the illustrative embodiments will be described usingterms commonly employed by those skilled in the art to convey thesubstance of their work to others skilled in the art. However, it willbe apparent to those skilled in the art that alternate embodiments maybe practiced with only some of the described aspects. For purposes ofexplanation, specific numbers, materials, and configurations are setforth in order to provide a thorough understanding of the illustrativeembodiments. However, it will be apparent to one skilled in the art thatalternate embodiments may be practiced without the specific details. Inother instances, well-known features are omitted or simplified in ordernot to obscure the illustrative embodiments.

Further, various operations will be described as multiple discreteoperations, in turn, in a manner that is most helpful in understandingthe illustrative embodiments; however, the order of description shouldnot be construed as to imply that these operations are necessarily orderdependent. In particular, these operations need not be performed in theorder of presentation.

The phrase “in one embodiment” is used repeatedly. The phrase generallydoes not refer to the same embodiment; however, it may. The terms“comprising,” “having,” and “including” are synonymous, unless thecontext dictates otherwise. The phrase “A/B” means “A or B”. The phrase“A and/or B” means “(A), (B), or (A and B)”. The phrase “at least one ofA, B and C” means “(A), (B), (C), (A and B), (A and C), (B and C) or (A,B and C)”. The phrase “(A)B” means “(B) or (A B)”, that is, A isoptional.

FIG. 1 illustrates an overview of the invention, in accordance withvarious embodiments. As illustrated, a computing device 102 may receiveat least one algorithmic expression 106 indirectly expressing aplurality of advertisements 104. In various embodiments, the algorithmicexpression 106 may have been defined on computing device 102 or onanother computing device based on one or both of user inputs and/orcomputational-linguistic methods. Logic 108 of the computing device 102may then generate an advertisement 104 (based on algorithmic expression106) for informational purposes, or may analyze the advertisementsimplicitly represented by algorithmic expression 106 in view of one ormore models of advertisement effectiveness to determine and generate anoptimal advertisement or advertisements 104, with what is considered“optimal” varying from embodiment to embodiment, depending on theeffectiveness model/function employed as well as the constraints, ifany, imposed. If generated to be informational, the advertisement 104may be deployed by logic 108 on a networking fabric 110, such as theInternet, to determine one or more metrics of effectiveness to associatewith the “optimal” advertisement(s) based on user reactions to theadvertisement 104. Such user reactions may be input through clients 112,which may include web browsers and other software and devices capable ofdetermining user reactions and providing those reactions to computingdevice 102. Computing device 102 may then store the determined metricsof effectiveness and their association with advertisement(s) 104 indatabase 114, enhancing, in some embodiments, the models ofadvertisement effectiveness to be employed for futureevaluations/assessments.

In one embodiment, client 112 may be a device adapted to render a mediaobject, the media object having one or more locations for the placementof advertisements 104. In such an embodiment, client 112 may be providedwith a plurality of advertisements 104 implicitly represented byalgorithmic expression 106 for one or more locations in the mediaobject, and may determine one or more advertisements for placement intoone or more locations within the media object by analyzing algorithmicexpression 106 in view of one or more user characteristic and/or usagepattern models.

In various embodiments, the computing device 102 described above maycomprise any single- or multi-processor or processor core centralprocessing unit (CPU) computing system. The computing device 102 may bea personal computer (PC), a workstation, a server, a router, amainframe, a modular computer within a blade server or high-densityserver, a personal digital assistant (PDA), an entertainment center, aset-top box, a media player, or a mobile device. Computing device 102may be capable of operating a plurality of operating systems (OS) in aplurality of virtual machines using virtualization technologies. Anexemplary single-/multi-processor or processor core computing device 102is illustrated by FIG. 3, and is described in greater detail below.Hereinafter, including in the claims, processor and processor core shallbe used interchangeable, with each term including the other. Any or allof the advertisements 104, algorithmic expression 106, and logic 108 maybe local to the computing device 102, as shown, or may be storedremotely and accessed via networking fabric 110 or another networkingfabric. Also, in various embodiments, database 114 and models ofadvertisement effectiveness may also be local to computing device 102.

In various embodiments, advertisements 104 may be any sort ofadvertisements known in the art. In one embodiment advertisements 104may comprise a plurality of ad copy to be displayed on a search resultspage returned by a search engine. As mentioned above, each ad copy maybe a few lines of text that are in turn associated with a link to abidding merchant's website. Exemplary ad copy include phrases such as“Find amazing interest rates and low costs—Apply now for fast approval!”and “Network of 1500 mortgage lenders—Find offers, regardless ofcredit.” In other embodiments, advertisements 104 may be banneradvertisements, audio segments, video segments, and/or audiovisualsegments. Such advertisements 104 may be placed in media objects, suchas television programs, video games, movies, and songs. In variousembodiments, advertisements 104 may be present on computing device 102,as shown. In other embodiments, advertisements 104 may be located on aremote device an may be retrieved by computing device 102 and/or clients112 for display on a search results page or placement within a mediaobject, for example. In one embodiment, advertisements 104 may be storedin database 114.

As shown, logic 108 of computing device 102 may receive or generate atleast one algorithmic expression 106. In various embodiments, the atleast one algorithmic expression 106 may be defined based on one or bothof user inputs and/or computational-linguistic methods. Computing device102 or another computing device may facilitate a user in defining thealgorithmic expression 106 by receiving user inputs. In someembodiments, where advertisements 104 are ad copy, algorithmicexpression 106 may be a combinatoric expression comprising a rule/basead copy having advertisement variables, each advertisement variableassociated with multiple advertisement variable values. The user mayprovide text inputs, such as terms or phrases, to build the rule, andmay even specify the entire expression 106. The computing device mayfacilitate the user in manually doing so through a word processingprogram, for example. In other embodiments, the algorithmic expression106 may comprise one or more of context free grammars, context sensitivegrammars, and/or general programs.

In other embodiments, the computing device 102 or another computingdevice may automatically define algorithmic expression 106. Thecomputing device may automatically define expression 106 by analyzingexisting advertisements 104 and/or through computational-linguisticmethods known in the art, such as image and speech recognition. In someembodiments, where advertisements 104 are audio and/or video segments,such as commercials, the computing device may automatically define acombinatoric decomposition of the advertisements 104. For example, ifthe advertisements include a car commercial, the combinatoricdecomposition may note the objects in the advertisement 104, such as thecar, and features of the object, such as a color, its model, traveldirection, speed, and so forth. This combinatoric decomposition may thenbe used to define the algorithmic expression 106, automatically creatinga rule for, e.g., the car object, the rule having a variable for color,and the variable color having multiple colors as values, including thecolor of the car in the advertisement 104 used for the combinatoricdecomposition. The rule may also have a variable for the model,traveling direction, speed etc., with each variable having a number orrange of permissible values. The resultant expression 106 may then beused to determined and generate the advertisement 104 with the carhaving a different model, color, traveling in one or more directions, atone or more speeds and so forth, and may deploy that advertisement 104to determine effectiveness metrics with the different color.

In one embodiment, computing device 102 may facilitate a user inreviewing the algorithmic expression 106 for legal purposes. Forexample, a user may want or need to review the expression 106 to makesure that it won't generate advertisements 104 which make claims thatthe advertiser cannot sustain.

In various embodiments, as described above, advertisements 104 may be adcopy. Ad copy, such as the examples given above, may be generated fromthe following algorithmic expression 106

-   -   “S→Find GOOD_ADJ Interest Rates for FINANCIAL_PRODUCT!!!        GOOD_ADJ→Fantastic, Great, Terrific, Amazing, Fabulous        FINANCIAL_PRODUCT→Home Loans, Mortgages, Refinancing.”        In such an expression, GOOD_ADJ and FINANCIAL_PRODUCT may be        advertising variables, and the terms fantastic, great, terrific,        amazing, fabulous, home loans, mortgages, and refinancing may be        advertising values, respectively, of the advertising variables.        In various embodiments, advertising variables may represent a        broad range of ad copy features, such as terms, phrases, nouns,        adjectives, verbs, adverbs, spacing, capitalizations, or numbers        and/or kinds of punctuation marks.

As is shown, computing device 102 may have logic 108 capable ofreceiving the at least one algorithmic expression 106 and/oradvertisements 104 via an interface of the computing device 102, such asa networking interface. In other embodiments, as described above,computing device 102 may automatically define algorithmic expression 106using computational-linguistic methods or facilitate a user in doing sothough user inputs. Upon receiving the combinatoric expression, logic108 may do one or both of (1) analyzing the expression 106 in view ofone or more models of advertisement effectiveness to determine andgenerate one or more advertisements 104 that are considered “optimal”,with what is considered “optimal” varying from embodiment to embodimentas described earlier; and/or (2) generating one or more advertisements104 based on the expression 106 to deploy the advertisement(s) 104 anddetermine one or more metrics of effectiveness of the advertisement(s)104 based on user reactions.

In embodiments, where logic 108 analyzes the expression 106 in view ofone or more models of advertisement effectiveness, logic 108 mayretrieve the one or more models from database 114. In alternateembodiments, the models may be integrated with logic 108. In someembodiments, where the expression 106 is associated with ad copyadvertisements 104, the models may be based on empirical evaluations ofprevious ad copy, and may be used to determine the “optimally” effectivead copy for one or more measures/scores of ad copy effectiveness, suchas click-through rates or conversions, with or without constraints, suchas biographic data or demographic information. In some embodiments,machine learning techniques, which may be used to direct a searchthrough the combinatoric space of an ad copy, may be used to find adcopy 104 that can be expected to have high metric scores when measuredempirically. Using such techniques may require assumptions about thekind of mathematical functions from which an advertisement'seffectiveness can be computed from its text. Upon finding such ad copy,logic 108 may generate one or more of them. In other embodiments, logic108 may first generate advertisements 104 and may then determine whichof the generated advertisements 104 is “optimal” by comparing theadvertisements to other advertisements for which effectiveness metricsare known.

In one embodiment, constraints/filters on effectiveness metrics, such asbiographical data of searchers, may include age, gender, income, andother relevant metrics useful for demographic grouping. The data may beplaced into demographically defined bins, the bins capable offacilitating demographic customizing of effectiveness metrics. Suchbiographic data may also be stored in and retrieved from database 114.

In embodiments where logic 108 generates advertisements 104 forinformational purposes, to be deployed, logic 108 may generate aplurality of advertisements corresponding to a set of combinations ofadvertisement values produced by the algorithmic expression 106, and maythen deploy each of these. In one embodiment, after generating all theadvertisements, logic 108 may determine if any are similar to knownadvertisements for which metrics are known, as described above. Thosethat are found to be similar need not be deployed. In one embodiment, analgorithm may be used in generating advertisements 104 to maximize theexpected information pay-off over time.

In various embodiments, the generated advertisements 104, such as adcopy, may then be deployed and evaluated to determine one or moreeffectiveness metrics/scores based on user reactions to theadvertisements 104. The ad copy may be deployed online and accessedthrough keyword searches on a search engine, such as Google or Overture.Effectiveness metrics/scores determined may include click-through rates,revenue generated, or other metrics often used in the art. Theclick-through rate and revenue generated may be very strong indicatorsof advertisement “success,” with what is considered “success” varyingfrom embodiment to embodiment. Effectiveness metrics determined may alsoinclude other indicators, such as mouse-over events for searchadvertisements, and data from passive gaze-tracking systems. Logic 108may monitor clients 112 to gather such metrics itself or may passivelyreceive the metrics from the clients 112. Upon determining new metricsfor an advertisement 104, logic 108 may store the metrics in database114 to further enhance models of advertising effectiveness.

In some embodiments, logic 108 may determine and generate a plurality ofcandidate advertisements 104 for placement within media objects having aplurality of locations where one of the advertisements 104 may be placedand presented with the media object. In such embodiments, logic 108 mayretrieve user characteristics and/or usage patterns from clients 112and/or database 114 and may analyze the algorithmic expression 106 inview of the user characteristics and/or usage patterns to determine theadvertisement to place within the media object. Such usercharacteristics and usage patterns may include viewer tastes,preferences, and habits. In one embodiment, logic 108 may select two ormore advertisements 104 and may auction the advertisement placement tothe highest bidding advertiser associated with the two or moreadvertisements 104. After logic 108 or the auction-winner/advertiserselect the advertisement to be placed in the media object, logic 108 maysend a bill to a broadcast server via networking fabric 110 notifyingthe advertiser associated with the presented advertisement 104 of thebilling event. In other embodiments, the advertisements 104 may havebeen determined and placed by clients 112, and logic 108 may simplyprovide the generated advertisement and algorithmic expression 106 tothe clients 112.

As illustrated, computing device 102 and clients 112 may be connected toa networking fabric 110. Networking fabric 110 may be any sort ofnetworking fabric known in the art, such as one or more of a local areanetwork (LAN), a wide area network (WAN), and the Internet. Computingdevice 102 and clients 112 may communicate via networking fabric 110 andmay further use any communication protocol known in the art, such as theHypertext Transfer Protocol (HTTP), and any transport protocol known inthe art, such as the Transmission Control Protocol/Internet Protocol(TCP/IP) suite of protocols. In various embodiments, networking fabric110 may also be connection to an application server providing searchservices. Such an application server may return search results pages toclients 112, the search results pages including advertisement 104,which, as described above, may be an ad copy. And in some embodiments,networking fabric may also connect one or both of computing device 102and clients 112 to a remote billing server to provide that server withnotifications of billing events.

In various embodiments, clients 112 may be any one or more computingdevices, peripheral devices, and/or processes known in the art. Clients112 may facilitate a user in passively and/or actively interacting withadvertisements 104. In some embodiments, where advertisements 104 are adcopy, clients 112 may comprise a web browser capable of receiving andrendering a search results page of a search engine that includesadvertisements 104. Such clients 112 may render the search results pagein response to a search by the user based on a word or words input bythe user. In some embodiments, computing device 102 or clients 112 maythen track reactions of the user to the advertisements, including mouseclicks to websites associated with the advertisements, transactions withthe advertiser associated with the advertisement, gazes of the user atthe advertisement (if clients 112 comprise a device capable of trackinguser eyeball positions and movements). If clients 112 are tracking userreactions, they may report those reactions to computing device 102 ordirectly to database 114 either periodically or in real-time. In otherembodiments, where advertisements 104 are commercials or some otheradvertisement type, media objects having the advertisements 104 mayallow users to “click through” (that is, in some way indicate interestor take action regarding) an advertisement 104 of the media object.

In some embodiments, clients 112 may be adapted to play media objectshaving a plurality of locations where one of a plurality of candidateadvertisements 104 may be placed and presented with the media object. Insuch embodiments, clients 112 may receive the media object along withthe plurality of candidate advertisements 104 and an algorithmicexpression 106. Clients 112 may then analyze the algorithmic expressions106 in view of user characteristics and/or usage patterns to determineone or more of the candidate advertisements 104 for placement in themedia object. As described above, such user characteristics and usagepatterns may include viewer tastes, preferences, and habits. In oneembodiment, clients 112 may select two or more advertisements 104 andmay auction the advertisement placement to the highest biddingadvertiser associated with the two or more advertisements 104. Afterclients 112 or the auction-winner/advertiser select the advertisement tobe placed in the media object, clients 112 may send a bill to abroadcast server via networking fabric 110 notifying the advertiserassociated with the presented advertisement 104 of the bill event.

As illustrated, database 114 may be remotely disposed on a databaseserver or other sort of computer system, or, in some embodiments, may bestored locally on computing device 102. Database 114 may be any sort ofdatabase known in the art, such as a relational database, a normalizeddatabase, a de-normalized database, or even an unstructured file. Asdescribed above, database 114 may store one or more metrics ofeffectiveness associated with advertisements 104, and may store eitheror both of information indicating the associations and/or theadvertisements 114 themselves. Such metrics and information may beprovided to database 114 by one of the computing device 102 or clients112 as the information and metrics are determined, and may then bestored by the database 114. Also, as described above, database 114 maystore one or more models of advertisement effectiveness, which mayinclude the metrics of effectiveness and, for example, machine learningmethods.

FIGS. 2 a-2 b are flow charts depicting various embodiments of theinvention. FIG. 2 a is a flow chart view of one embodiment of theinvention, where an algorithmic expression is analyzed by logic of acomputing device in view of one or more models of advertisingeffectiveness to determine and generate an advertisement. Asillustrated, the computing device may first receive at least onealgorithmic expression associated with a plurality of advertisements,block 202, the algorithmic expression defined based on one or both ofuser inputs and/or computational-linguistic methods. In variousembodiments, the plurality of advertisements may be ad copy and the atleast one algorithmic expression may be a combinatoric expressioncomprising a base ad copy and a plurality of advertisement variables. Insuch embodiments, the plurality of advertisement variables may includeat least one of a term, a phrase, a noun, an adjective, a verb, anadverb, spacing, capitalization, or number and/or kind of punctuationmarks. In other embodiments, the plurality of advertisements may becandidate advertisements for placement within a media object, and themodels of advertisement effectiveness may include user characteristicsand/or usage patterns.

Upon receiving the at least one algorithmic expression, logic of thecomputing device may either (1) analyze the algorithmic expression inview of one or more models of advertising effectiveness to determine anadvertisement, block 204, and then generate the determinedadvertisement, block 206, or (2) first generate a plurality of possibleadvertisements, such as ad copy, having differing values for anadvertisement variable of the at least one algorithmic expression, block208, and then compare the advertisements to other advertisements forwhich effectiveness metrics are known, block 210. In variousembodiments, the one or more models of advertisement effectiveness maybe based on previous empirical evaluations of other advertisements, andthe other advertisements may be determined by machine learning methods.Further, in some embodiments, the effectiveness metrics may includeclick-through rate or conversions.

FIG. 2 b is a flow chart view of another embodiment of the invention,where an advertisement is generated to be informational, and is deployedto determine one or more metrics of effectiveness based on userreactions. As illustrated, a computing device may either (1) receive anadvertisement generated based on at least one algorithmic expression fora plurality of advertisements, block 212, or (2) may receive the atleast one algorithmic expression for the plurality of advertisements,block 214, the expression defined based on one or both of user inputsand/or computational-linguistic methods, and may itself generate theadvertisement based on the expression, block 216. In some embodiments,the computing device may generate the plurality of advertisements,including the advertisement, based on a plurality of combinations ofvalues of at least one advertisement variable of the at least onealgorithmic expression. In one embodiment, the plurality ofadvertisements may be ad copy and the at least one algorithmicexpression may be a combinatoric expression comprising a base ad copyand a plurality of advertisement variables. In such an embodiment, theplurality of advertisement variables may include at least one of a term,a phrase, a noun, an adjective, a verb, an adverb, spacing,capitalization, or number and/or kind of punctuation marks.

Upon receiving or generating the advertisement, the computing device maydeploy the advertisement to determine one or more metrics ofeffectiveness to be associated with the advertisement based on userreactions to the advertisement, block 218. In various embodiments, theadvertisement may be deployed online and accessible through keywordsearches on search engines. In further embodiments, the one or moremetrics of effectiveness may include at least one of click-throughrates, conversions, revenue generated, mouse-over events, and passivegaze-tracking, and the user reactions may include at least one ofmouse-clicks, gazing, and transactions. After deploying theadvertisement to determine the metrics of effectiveness, the computingdevice may store the effectiveness metrics in a database, block 220, toenhance the above described one or more models of advertisementeffectiveness.

FIG. 3 illustrates an exemplary computing device capable of performingthe operations of various embodiments of the present invention. Asshown, computing system/device 300 may include one or more processors302, and system memory 304. Additionally, computing system/device 300may include mass storage devices 306 (such as diskette, hard drive,CDROM and so forth), input/output devices 308 (such as keyboard, cursorcontrol and so forth) and communication interfaces 310 (such as networkinterface cards, modems and so forth). The elements may be coupled toeach other via system bus 312, which represents one or more buses. Inthe case of multiple buses, they may be bridged by one or more busbridges (not shown).

System memory 304 and mass storage 306 may be employed to store aworking copy and a permanent copy of the programming instructionsimplementing one or more aspects of the above described teachings topractice the present invention, such as computational logic 314. Theprogramming instructions may be implemented in assembler instructionssupported by processor(s) 302 or high level languages, such as C, thatmay be compiled into such instructions.

The permanent copy of the programming instructions may be placed intopermanent storage 306 in the factory, or in the field, through e.g. adistribution medium (not shown) or through communication interface 310(from a distribution server (not shown)).

Although specific embodiments have been illustrated and described hereinfor purposes of description of the preferred embodiment, it will beappreciated by those of ordinary skill in the art that a wide variety ofalternate and/or equivalent implementations may be substituted for thespecific embodiment shown and described without departing from the scopeof the present invention. Those with skill in the art will readilyappreciate that the present invention may be implemented in a very widevariety of embodiments. This application is intended to cover anyadaptations or variations of the embodiments discussed herein.Therefore, it is manifestly intended that this invention be limited onlyby the claims and the equivalents thereof.

What is claimed is:
 1. A method comprising: generating, by a computingdevice, at least one combinatoric algorithmic expression of a pluralityof audiovisual segment advertisements, wherein the combinatoricalgorithmic expression comprises a base ad audiovisual segment copy anda plurality of advertisement variables, wherein the combinatoricalgorithmic expression defines combinations of elements of the base adaudiovisual segment copy, wherein at least one of the advertisementvariables controls one or more movements of one or more visual elementswithin the audiovisual segment copy; analyzing, by the computing device,the at least one combinatoric algorithmic expression in view of one ormore models of advertisement effectiveness with respect to each of theplurality of advertisement variables for combining potential elements ofthe base ad audiovisual segment copy; selecting via the at least onecombinatoric algorithmic expression a combination of one or more of thepotential elements of the base ad audiovisual segment copy as anadvertisement of the plurality of advertisements; and generating theadvertisement from the combination of the one or more of the potentialelements of the base ad audiovisual segment copy based on the selecting.2. The method of claim 1, wherein the at least one combinatoricalgorithmic expression is defined based on one or both of user inputsand computational-linguistic methods.
 3. The method of claim 1, whereinsaid analyzing further comprises generating a number of ad copy, havingdiffering values for an advertisement variable of the combinatoricalgorithmic expression, and comparing each ad copy to at least one otherad copy for which one or more effectiveness metrics are known.
 4. Themethod of claim 1, wherein the plurality of advertisements are candidateadvertisements for placement within a media object, and the one or moremodels of advertisement effectiveness include user characteristics andusage patterns.
 5. The method of claim 1, wherein the one or more modelsof advertisement effectiveness are based on previous empiricalevaluations of other advertisements, and the other advertisements aredetermined by machine learning methods.
 6. The method of claim 1,wherein the selecting is based at least in part on conversions.
 7. Themethod of claim 1, wherein the audiovisual segment contains both audioand moving images.
 8. The method of claim 1, wherein the combinatoricalgorithmic expression describes color compositions of one or morevisual elements within the audiovisual segment copy.
 9. The method ofclaim 1, wherein the combinatoric algorithmic expression describes anaudiovisual segment copy for placement in an audiovisual media object.10. The method of claim 1, wherein the method further comprisesfacilitating a review of the combinatoric algorithmic expression forlegal purposes.
 11. A non-transitory computer-readable storage mediumstoring program instructions, wherein the program instructions arecomputer-executable to implement: generating, by a computing device, atleast one combinatoric algorithmic expression of a plurality ofadvertisements, wherein the combinatoric algorithmic expressioncomprises a base ad audiovisual segment copy and a plurality ofadvertisement variables, wherein the combinatoric algorithmic expressiondefines combinations of elements of the base ad audiovisual segmentcopy, wherein at least one of the advertisement variables controls oneor more movements of one or more visual elements within the audiovisualsegment copy; analyzing, by the computing device, the at least onecombinatoric algorithmic expression in view of one or more models ofadvertisement effectiveness with respect to each of the plurality ofadvertisement variables for combining potential elements of the base adaudiovisual segment copy; selecting via the at least one combinatoricalgorithmic expression a combination of one or more of the potentialelements of the base ad audiovisual segment copy as an advertisement ofthe plurality of advertisements; and generating the advertisement fromthe combination of the one or more of the potential elements of the basead audiovisual segment copy based on the selecting.
 12. Thenon-transitory computer-readable storage medium of claim 11, furthercomprising program instructions computer-executable to implementdeploying, by the computing device, the advertisement to determine oneor more metrics of effectiveness to be associated with the advertisementbased on user reactions to the advertisement.
 13. The non-transitorycomputer-readable storage medium of claim 11, wherein said programinstructions computer-executable to implement said analyzing furthercomprise program instructions computer-executable to implementgenerating a number of ad copy having differing values for anadvertisement variable of the combinatoric algorithmic expression, andcomparing each ad copy to at least one other ad copy for which one ormore effectiveness metrics are known.
 14. The non-transitorycomputer-readable storage medium of claim 11, wherein the plurality ofadvertisement variables include at least one of a term, a phrase, anoun, an adjective, a verb, an adverb, spacing, capitalization, ornumber and kind of punctuation marks.
 15. The non-transitorycomputer-readable storage medium of claim 11, wherein the one or moremetrics of effectiveness include at least one of click-through rates,conversions, revenue generated, mouse-over events, and passivegaze-tracking, and the user reactions include at least one ofmouse-clicks, gazing, and transactions.
 16. The non-transitorycomputer-readable storage medium of claim 11, wherein the advertisementis deployed online and is accessible through keyword searches on searchengines.
 17. The non-transitory computer-readable storage medium ofclaim 11, further comprising storing the one or more metrics ofeffectiveness in a database.
 18. An apparatus comprising: a processor;and a memory comprising program instructions, wherein the programinstructions are executable by the at least one processor to: generateat least one combinatoric algorithmic expression of a plurality ofadvertisements, wherein the combinatoric algorithmic expressioncomprises a base ad audiovisual segment copy and a plurality ofadvertisement variables, wherein the combinatoric algorithmic expressiondefines combinations of elements of the base ad audiovisual segmentcopy, wherein at least one of the advertisement variables controls oneor more movements of one or more visual elements within the audiovisualsegment copy; analyze the at least one combinatoric algorithmicexpression in view of one or more models of advertisement effectivenesswith respect to each of the plurality of advertisement variables forcombining potential elements of the base ad audiovisual segment copy;select via the at least one combinatoric algorithmic expression acombination of one or more of the potential elements of the base adaudiovisual segment copy as an advertisement of the plurality ofadvertisements; and generate the advertisement from the combination ofthe one or more of the potential elements of the base ad audiovisualsegment copy based on the selecting.
 19. The apparatus of claim 18,wherein the plurality of advertisements are ad copy and the at least onecombinatoric algorithmic expression comprises a base ad copy and aplurality of advertisement variables.
 20. The apparatus of claim 18,wherein the one or more metrics of effectiveness include at least one ofclick-through rates, conversions, revenue generated, mouse-over events,and passive gaze-tracking, and the user reactions include at least oneof mouse-clicks, gazing, and transactions.